QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter

Xiwu Han, Gregory Toner


Abstract
This paper presents our submission to SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Naïve Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B.
Anthology ID:
S17-2063
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
380–384
Language:
URL:
https://aclanthology.org/S17-2063
DOI:
10.18653/v1/S17-2063
Bibkey:
Cite (ACL):
Xiwu Han and Gregory Toner. 2017. QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 380–384, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter (Han & Toner, SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2063.pdf